{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Set path to TTim (if needed)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ttimpath = '/Users/mark/ttim/svn/trunk'\n",
      "import sys\n",
      "if ttimpath not in sys.path: sys.path.append(ttimpath)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Start pylab and import ttim"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "from ttim import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a quasi-3d model with 5 layers"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ml = Model3D(kaq = 1, z = arange(5,-1,-1), Saq = 0.001, kzoverkh = 0.1, phreatictop = False, tmin = 1, tmax = 10, M = 20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a ditch consisting of 9 line-sinks (in this case along a line with shorter line-sinks towards the ends), with a Q of 20 m$^3$/d, a resistance to inflow of 1 day and the width of inflow equal to the thickness of the layer that the ditch is screened in. The ditch is screened in layers 1 and 2."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 10 * cos( linspace(-pi,0,10) )\n",
      "y = zeros(10)\n",
      "lsd = MscreenLineSinkDitchString(ml, xy = zip(x,y), tsandQ = [(0.0, 20.0)], res = 1.0, wh = 'H', layers = [1,2] )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Solve the model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ml.solve()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "self.Neq  18\n",
        "solution complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The line-sink ditch has a function that returns the head inside the ditch at the specified times. It returns an array of size (nls,nlay,ntimes). The heads should all be approximately equal. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "headinside = lsd.headinside(t=3)\n",
      "print headinside"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[[-2.88781124]\n",
        "  [-2.88781125]]\n",
        "\n",
        " [[-2.88781117]\n",
        "  [-2.88781131]]\n",
        "\n",
        " [[-2.88781117]\n",
        "  [-2.88781121]]\n",
        "\n",
        " [[-2.88781137]\n",
        "  [-2.88781135]]\n",
        "\n",
        " [[-2.88781138]\n",
        "  [-2.88781128]]\n",
        "\n",
        " [[-2.88781132]\n",
        "  [-2.88781123]]\n",
        "\n",
        " [[-2.88781132]\n",
        "  [-2.88781125]]\n",
        "\n",
        " [[-2.88781132]\n",
        "  [-2.88781127]]\n",
        "\n",
        " [[-2.88781128]\n",
        "  [-2.88781125]]]\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The ditch also has a `strength` function, which returns the discharge of the entire line-sink ditch for each layer. Besides the `strength` function there is a `strength_list`, which returns the individual discharge of each line-sink of the line-sink string. It can be verified that the solution is correct by comparing the discharge of each line-sink with the discharge computed by taking the difference between the head just on the outside of the line-sink minus the head inside the linesink and divide the head difference by the resistance factor stored by TTim (equal to `res / (wh * L)`, where `res` is the specified resistance (in days), and `wh` is the width or height over which water enters the ditch, and `L` is the length of each line-sink. The computed arrays for the head on the inside and outside have different shapes (there is a reason for that, but it is a bit inconvenient). "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hin = lsd.headinside(t=3) # shape (nls,nlay,ntimes)\n",
      "hout = ml.headalongline( lsd.xc, lsd.yc, 3, [1,2] ) # shape (nlay,ntimes,nls)\n",
      "resfach = lsd.resfach.reshape((lsd.Nls,2))\n",
      "print 'Q computed from head difference'\n",
      "for ils in range(lsd.Nls):\n",
      "    print ( hout[:,0,ils] - hin[ils,:,0] ) / resfach[ils]\n",
      "Qlist = lsd.strength_list(t=3)\n",
      "print 'Q computed by TTim solve'\n",
      "print Qlist[:,:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Q computed from head difference\n",
        "[ 0.44735183  0.49483617]\n",
        "[ 0.97594445  1.12087082]\n",
        "[ 1.18455783  1.42880514]\n",
        "[ 1.28085495  1.58985951]\n",
        "[ 1.31220508  1.6416332 ]\n",
        "[ 1.28085491  1.58985957]\n",
        "[ 1.18455783  1.42880514]\n",
        "[ 0.97594452  1.12087078]\n",
        "[ 0.44735181  0.49483619]\n",
        "Q computed by TTim solve\n",
        "[[ 0.44735183  0.49483617]\n",
        " [ 0.97594445  1.12087082]\n",
        " [ 1.18455783  1.42880514]\n",
        " [ 1.28085495  1.58985951]\n",
        " [ 1.31220508  1.6416332 ]\n",
        " [ 1.28085491  1.58985957]\n",
        " [ 1.18455783  1.42880514]\n",
        " [ 0.97594452  1.12087078]\n",
        " [ 0.44735181  0.49483619]]\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Cross-section of head in layer 1 along and beyond the ditch"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xsection( ml, -20, 20, 0, 0, N = 100, t = [1,5,10], layers = 1 )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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P7uJh+igujlbLqlVL/WPce3YPo/ePxpqP1qg3Hv/RI2qcdexIpdT1vPa9Npje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\njDETwkmfMcZMyP8B//CTarewp6gAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x30257d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compute the head inside the line-sink for time varying from 1 to 10. Only store the head in the first line-sink of the ditch, as they are all the same anyway."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "head1 = lsd.headinside(linspace(1,10,50))[0,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create the same model, but now add storage in the canal by specifying the storage area `Astorage=20.0`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ml = Model3D(kaq = 1, z = arange(5,-1,-1), Saq = 0.001, kzoverkh = 0.1, phreatictop = False, tmin = 1, tmax = 10, M = 20)\n",
      "lsd = MscreenLineSinkDitchString(ml, xy = zip(x,y), tsandQ = [(0.0, 20.0)], res = 1.0, wh = 'H', layers = [1,2], Astorage = 20.0 )\n",
      "ml.solve()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "self.Neq  18\n",
        "solution complete"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'inflow into ditch at time t=3', sum(lsd.strength(t=3))\n",
      "print 'rate of lowering of water table', -lsd.headinside(t=3,derivative=1)[0,0,0] * lsd.Astorage\n",
      "print 'total discharge ',sum(lsd.strength(t=3)) - lsd.headinside(t=3,derivative=1)[0,0,0] * lsd.Astorage"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "inflow into ditch at time t=3 13.0700091249\n",
        "rate of lowering of water table "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "6.9299910671\n",
        "total discharge  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "20.000000192\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Head in canal for case with canal storage"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "head2 = lsd.headinside(linspace(1,10,50))[0,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(linspace(1,10,50),head1,label='w/o  canal storage')\n",
      "plot(linspace(1,10,50),head2,label='with canal storage')\n",
      "xlabel('time (days)')\n",
      "ylabel('drawdown in canal (m)')\n",
      "legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.legend.Legend at 0x35cd7b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vv5ZXKWTdNTAA/kx+Sy4uwZ0Xd3B2+lmaX4MQ0mRCD4whQ4YgJCQEkpKSrS6u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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x35e7b10>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hin = lsd.headinside(t=3) # shape (nls,nlay,ntimes)\n",
      "hout = ml.headalongline( lsd.xc, lsd.yc, 3, [1,2] ) # shape (nlay,ntimes,nls)\n",
      "resfach = lsd.resfach.reshape((lsd.Nls,2))\n",
      "print 'Q computed from head difference'\n",
      "for ils in range(lsd.Nls):\n",
      "    print ( hout[:,0,ils] - hin[ils,:,0] ) / resfach[ils]\n",
      "Qlist = lsd.strength_list(t=3)\n",
      "print 'Q computed by TTim solve'\n",
      "print Qlist[:,:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Q computed from head difference\n",
        "[ 0.29155706  0.32249177]\n",
        "[ 0.63669181  0.73114541]\n",
        "[ 0.77414124  0.93343155]\n",
        "[ 0.83842843  1.04008987]\n",
        "[ 0.85949943  1.07455534]\n",
        "[ 0.83842843  1.04008989]\n",
        "[ 0.77414124  0.93343153]\n",
        "[ 0.63669181  0.73114539]\n",
        "[ 0.29155707  0.32249178]\n",
        "Q computed by TTim solve\n",
        "[[ 0.29155706  0.32249177]\n",
        " [ 0.63669181  0.73114541]\n",
        " [ 0.77414124  0.93343155]\n",
        " [ 0.83842843  1.04008987]\n",
        " [ 0.85949943  1.07455534]\n",
        " [ 0.83842843  1.04008989]\n",
        " [ 0.77414124  0.93343153]\n",
        " [ 0.63669181  0.73114539]\n",
        " [ 0.29155707  0.32249178]]\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    }
   ],
   "metadata": {}
  }
 ]
}